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UCI Seeds Dataset
Load the UCI Seeds dataset in Python with one line of code in seconds and plug it in TensorFlow and PyTorch with Activeloop Hub.

UCI Seeds Dataset

What is UCI Seeds Dataset?

The UCI Seeds dataset was originated from the properties of a three different varieties of wheat such as Kama, Rosa, and Canadian. Each category of wheat has 70 elements which were randomly selected for the experiment. The dataset contains the properties of the images which were recorded on X-ray KODAK plates. This dataset can be used for classification and clustering purposes.

Downloading UCI Seeds Dataset in Python

Instead of downloading the UCI Seeds in Python, you can effortlessly load it in Python via our open-source package Hub with just one line of code.

Load UCI Seeds Dataset Subset in Python

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import hub
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ds = hub.load('hub://activeloop/seeds-uci')
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UCI Seeds Dataset Structure

Data Fields

  • area_A: tensor containing area of the wheat grains
  • perimeter_P: tensor containing perimeter of the wheat grains
  • compactness_C: tensor containing compactness of the wheat grains
  • length_of_kernel: tensor containing length of each wheat kernel
  • width_of_kernel: tensor containing width of each wheat kernel
  • asymmetry_coefficient: tensor containing asymmetry coefficient of wheat kernel
  • length_of_kernel_groove: tensor containing length of a kernel groove

How to use UCI Seeds Dataset with PyTorch and TensorFlow in Python

Train a model on UCI Seeds dataset with PyTorch in Python

Let's use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:
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dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
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Train a model on UCI Seeds dataset with TensorFlow in Python

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dataloader = ds.tensorflow()
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Additional Information about UCI Seeds Dataset

UCI Seeds Dataset Description

UCI Seeds Dataset Contributors

M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak

UCI Seeds Dataset Licensing Information

Hub users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.
If you're a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

UCI Seeds Dataset Citation Information

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@incollection{charytanowicz2010complete,
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title={Complete gradient clustering algorithm for features analysis of x-ray images},
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author={Charytanowicz, Ma{\l}gorzata and Niewczas, Jerzy and Kulczycki, Piotr and Kowalski, Piotr A and {\L}ukasik, Szymon and {\.Z}ak, S{\l}awomir},
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booktitle={Information technologies in biomedicine},
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pages={15--24},
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year={2010},
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publisher={Springer}
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}
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